Social Simulation – an introduction Bruce Edmonds Centre for Policy Modelling Manchester...

Social Simulation – an introduction

Bruce EdmondsCentre for Policy Modelling

Manchester Metropolitan University

About Modelling

What is a model?

Something, A, that is used to understand or answer questions about something else, B

• e.g: A scale model to test in a wind tunnel• e.g: The official accounts of a business• e.g: The minutes of a meeting• e.g: A flow chart of a legal process• e.g: A memory of a past event• e.g: A computer simulation of the weather• e.g: The analogy of fashion as a virus

Models usually abstract certain features and have other features that are irrelevant to what is modelled

An Introduction to SS. By Bruce Edmonds, ISS Course, 2011, slide 3

A simple consequence of this…

• That if you are only exploring a model to find out about the model, then this is useless, unless…:

• This understanding helps one understand other models, for example:– An idea about something – this is generally private

but not publically useful knowledge– Or is of SUCH generality it informs us about SO

many other models that it is worth adsorbing• Normally we use a model to tell us about

something else, something observed (maybe via intermediate models, such as data)


What is a formal model?

Something that (in theory) can be written down precisely, whose content is

specified without ambiguity• e.g: mathematical/statistical relations,

computer programs, sets of legal rules

Can make exact copies of it

Agreed rules for interpreting/using them

Can make certain inferences from them• Not: an analogy, a memory, a physical thing


The Model and its Target

• A formal model is not a model at all without this mapping relation telling us the intended meaning of its parts

Object System

Model

The mapping between formal

model and what the parts refer to


A Model used for prediction of unknown data

Object Systemknown unknown

Modelinput

(parameters, initial conditions etc.)

output(results)

encoding(measurement)

decoding(interpretation)

Inference using model


A Model used for explanation of known data in terms of mapping

Object Systemknown unknown

Modelinput

(parameters, initial conditions etc.)

output(results)

encoding(measurement)

decoding(interpretation)

Inference using model


Explanation is the outcomes in

terms of the process and initial state

Model is adjusted until the outcomes

map to the results

The Whole Modelling Chain

• In both prediction and explanation…• to get anything useful out…• One has to traverse the whole modelling chain,

three steps:1. From target system to model

2. Inference using the model

3. From model back to target system• The “usefullness” of the model, roughly speaking,

comes from the strength of the whole chain• If one strengths one part only to critically weaken

another part this does not help


Modelling Purposes

All modelling has a purpose (or several)

Including:• Description• Prediction• Establishing/suggesting explanations• Illustration/communication• Exploration• Analogy

These are frequently conflated!


The Modelling Context

All modelling has a context• The background or situation in which the

modelling occurs and should be interpreted• Whether explicit or (more normally) implicit• Usually can be identified reliably but not

described precisely and completely• The context inevitably hides many implicit

assumptions, facts and processes

Modelling only works if there is a reliably identifiable context to model within


Analytic formal models

Where the model is expressed in terms that allow for formal inferences about its

general properties to be made• e.g. Mathematical formulae• Where you don’t have to compute the

consequences but can derive them logically• Usually requires numerical representation of

what is observed (but not always)Only fairly “simple” mathematical models can be

treated analytically – the rest have to be simulated/calculated


Equation-based or statistical modelling

Real World Equation-based Model

Actual Outcomes

AggregatedActual Outcomes

AggregatedModel Outcomes


Computational models

Where a process is modelled in a series of precise instructions (the program) that can

be “run” on a computer• The same program always produces the same

results (essentially) but...• ...may use a “random seed” to randomise

certain aspects• Can be simple or very complex• Often tries to capture more “qualitative” aspects

of phenomena• A computational model of social phenomena is

a social simulationAn Introduction to SS. By Bruce Edmonds, ISS Course, 2011, slide 14

Origins of Social Simulation

(Occasionally) Interacting Streams:– Sociology, including social network analysis– Distributed Computer Science Programming

Languages – Artificial Intelligence & Machine Learning– Ecological Modelling

(Strangely) Not much from:– (Mainstream) Economics– Cognitive Modelling– Numerical Simulation– System Dynamics


Two Different Directions

1. Towards the detailed interaction between entities– Trying to capture how the complex interaction

between decision-making actors might result in the “unexpected” emergence of outcomes

– Roughly this is Agent-based simulation

2. Towards the detail of circumstance– Trying to use data that allows different regions

or cases to be captured by different models– Roughly this is Microsimulation


Other kinds of social simulation model• Cellular Automaton Models – where patches in a

surface change state in response to their neighbours’ states

• System Dynamic Models – where a system of equations representing top-level, aggregate variables are related, then computationally simulated (sometimes with animation)

• Population Dynamics Models – where a statistical distribution represents a collection of individuals plus how these distributions change over time


A little bit aboutMicrosimulation

About Microsimulation

• Instead of having a generic process over all relevant situations one has a model for each situation

• This is limited and determined by available data for each of these situations

• Often these situations are geographical regions• Often each model is a population dynamics model with a different

distribution for each region, trained on available data (usually each distribution come from a family which encode assumptions about the processes)

• Thus variation is not handled by some generic “noise” but rather aggregation is put off to a post-hoc summary of the complex results retaining the context-specificity

• This approach is heavily data-driven• You have to look at each separate region to determine if the local

model is a good fit in each case


Microsimulation

Observed World Computational Model

Outcomes Model Outcomes

AggregatedOutcomes



Example 1: General Election Forecasting• John Curtice

(Strathclyde) and David Firth (Warwick) (+ input from others)

• Each constituency is statistically modelled as a three-way split (Lab, Con, LD) based on how much this swung with the general trend according to past data


Example 1: General Election Forecasting

• Each line is the 3-way vote share for each constituency in UK general elections,

• green spots show 2005 shares, tail is the 2001 shares

Pros and Cons of Microsimulation


• Data-driven• Allows for local

differences (context-sensitive)

• Assumptions are statistical rather than behavioural

• Relates well to maps and hence results are readily communicable

• Needs a lot of data at the granularity being modelled

• Does not (without extension) capture interactions between regions

• Can take a lot of computer power

• Does not result in a simple explanation or abstraction

Advantages Disadvantages

Much more aboutAgent-Based Social Simulation

Some Key Historical Figures

• Herbert Simon– Observed administrative behaviour and described

it using algorithms – ‘procedural rationality’ (rather than optimisation of utility)

– Also (with Alan Newell) produced first computational models of aspects of cognition

• Thomas Schelling– A simple but effective example of individual-based

modelling (in the coming slides) showing power of simulation establishing a micro-macro link

• Mark Granovetter– Distinguished the importance of tracing individual

interactions, ‘social embeddedness’– Highlighted such processes and structure (‘ties’)


Individual-based simulation

Observed World Computational Model

Outcomes Model Outcomes

AggregatedOutcomes


Agent-


Micro-Macro Relationships

Micro/ Individual data Qualitative, behavioural, social psychological data

Theory, narrative accounts

Social, economic surveys; Census Macro/ Social data

Simulation


Characteristics of agent-based modelling• Computational description of process• Not usually analytically tractable • More context-dependent…• … but assumptions are much less drastic• Detail of unfolding processes accessible

– more criticisable (including by non-experts)• Used to explore inherent possibilities• Validatable by data, opinion, narrative ...• Often very complex themselves


What happens in ABSS

• Entities in simulation are decided up• Behavioural Rules for each agent specified (e.g. sets of

rules like: if this has happened then do this)• Repeatedly evaluated in parallel to see what happens• Outcomes are inspected, graphed, pictured, measured

and interpreted in different ways

Simulation

Representations of OutcomesSpecification (incl. rules)


Example 2: Schelling’s Segregation ModelSchelling, Thomas C. 1971. Dynamic Models of Segregation. Journal of Mathematical Sociology 1:143-186.

Rule: each iteration, each dot looks at its neighbours and if less than 30% are the same colour as itself, it moves to a random empty square

Conclusion: Segregation can result from wanting only a few neighbours of a like colour


Simple, Conceptual Simulations Such as Schelling’s• Are highly suggestive• Once you play with them, you start to “see” the

world in terms of you model – a strong version of Kuhn’s theoretical spectacles

• They can help persuade beyond the limit of their reliability

• They may well not be directly related to any observations of social phenomena

• Are more a model of an idea than any observed phenomena

• Can be used as a counter-example


Modelling a concept of something

Phenomena

conceptual model

Model

Exploration with model

AnalogicalApplication


Some Criteria for Judging a Model

• Soundness of design– w.r.t. knowledge of how the object works– w.r.t. tradition in a field

• Accuracy (lack of error)• Simplicity (ease in communication,

construction, comprehension etc.)• Generality (when you can safely use it)• Sensitivity (relates to goals and object)• Plausibility (of design, process and results)• Cost (time, effort, etc.)


Some modelling trade-offs

simplicity

generality

Lack of error (accuracy of outcomes)

realism(design reflects observations)


Example 3: A model of social influence and water demand

• Investigate the possible impact of social influence between households on patterns of water consumption

• Design and detailed behavioural outcomes from simulation validated against expert and stakeholder opinion at each stage

• Some of the inputs are real data• Characteristics of resulting aggregate time

series validated against similar real data


Type, context, purpose

• Type: A complex agent-based descriptive simulation integrating a variety of streams of evidence

• Context: statistical and other models of domestic water demand under different climate change scenarios

• Purposes: – to critique the assumptions that may be implicit

in the other models– to demonstrate an alternative


Simulation structure

• Activity

• Frequency

• Volume Households

Policy

Agent

• Temperature

• Rainfall

• Daylight

Ground

Aggregate Demand

• Activity

• Frequency

• Volume Households

Policy

Agent

• Temperature

• Rainfall

•

Ground

Aggregate Demand


Some of the household influence structure

- Global Biased- Locally Biased- Self Biased

Example results

Aggregate demand series scaled so 1973=100

0

20

40

60

80

100

120

140

160

180

200

J-73

J-74

J-75

J-76

J-77

J-78

J-79

J-80

J-81

J-82

J-83

J-84

J-85

J-86

J-87

J-88

J-89

J-90

J-91

J-92

J-93

J-94

J-95

J-96

J-97

Simulation Date

Re

lative

De

ma

nd


Conclusions from Water Demand Example• The use of a concrete descriptive simulation

model allowed the detailed criticism and, hence, improvement of the model

• The inclusion of social influence resulted in aggregate water demand patterns with many of the characteristics of observed demand patterns

• The model established how it was possible that processes of mutual social influence could result in widely differing patterns of consumption that were self-reinforcing


What ABSS Can Do

• ABSS can allow the production and examination of sets of possible complicated processes both emergent and immergent

• Using a precise (well-defined and replicable) language (a computer program)

• But one which allows the tracing of very complicated interactions

• And thus does not need the strong assumptions that analytic approaches require to obtain their proofs

• It allows the indefinite experimentation and examination of outcomes (in vitro)

• Which can inform our understanding of some of the complex interactions that may be involved in observed (in vivo) social phenomena


Conclusion

The in vitro and in vivo analogy

• In vivo is what happens in real life, e.g. between complex chemicals in the cell– Any data or experiments here involve the whole

complex context of the target system– But these are often so complex its impossible to

detangle the interactions at this level• In vitro is what happens in the test tube with

selected chemicals, it is a model of of the cell– This allows experiments and probes to tease out

how some of the complex interactions occur– But you never know if back in the cell these may be

overwhelmed or subverted by other interactionsAn Introduction to SS. By Bruce Edmonds, ISS Course, 2011, slide 43

Discursive vs Simulation Approaches

• Rich, semantic, meaningful, flexible

• But imprecise• Map to what is observed

is often complex and implicit

• Difficult to keep track of complicated interactions and outcomes

• Has “pre-prepared” meaning and referents

• Precise, well defined, replicable, flexible

• But brittle• Semantically thin• Map to observed can be

explicit and more direct• Good at keeping track of

complicated interactions and outcomes

• Meaning needs to be established through use

Natural Language Computer Simulation


Analytic vs Simulation Approaches

• Precise, well defined, replicable

• Very brittle• Not Semantic• Map to observed can be

indirect and/or difficult to establish

• Strong checkable inference • General characterisation of

outcomes• Requires strong

assumptions to work

• Precise, well defined, replicable, flexible

• More expressive descriptive

• Semantically thin• Map to observed can be

explicit and more direct• Inference is more

contingent, (sets of) example outcomes

• Can relate more easily to a broader range of evidence

Analytic Modelling Computer Simulation


The End

These slides are accessible from the ‘slides’ link on theIntroduction to Social Simulation Course Page

http://sites.google.com/site/socialsimulationcourse

Bruce Edmondshttp://bruce.edmonds.nameCentre for Policy Modelling

http://cfpm.org Manchester Metropolitan Business School

http://www.business.mmu.ac.ukNeISS Portal

http://www.neiss.org.uk

Date post:	26-Dec-2015
Category:	Documents
Upload:	harold-greer
View:	215 times
Download:	0 times

Social Simulation – an introduction Bruce Edmonds Centre for Policy Modelling Manchester...

Documents